Merging Rank Lists from Multiple Sources in Video Classification

Multimedia corpora increasingly consist of data from multiple sources, with different characteristics that can be exploited by specialized applications. This paper focuses on video classification over multiple-source collections, and addresses the question whether classifiers should train from individual sources or from a full data set across all sources. If training separately, how can rank lists from different sources be merged effectively? We formulate the problem of merging ranked lists as learning a function mapping from local scores to global scores, and propose a learning method based on logistic regression. In our experiments we find that source characteristics are very important for video classification. Moreover, our method of learning mapping functions performs significantly better than merging methods without explicitly learning the mapping junctions